Academic Editors: Vitor Caldeirinha,

João Correia, Maria Teresa Folgôa

Batista and Tiago Pinho

Received: 27 November 2024

Revised: 26 December 2024

Accepted: 30 December 2024

Published: 4 January 2025

Citation: Gonçalves, M.; Salgado, C.;

de Sousa, A.; Teixeira, L. Data

Storytelling and Decision-Making in

Seaport Operations: A New Approach

Based on Business Intelligence.

Sustainability 2025, 17, 337. https://

doi.org/10.3390/su17010337

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license

(https://creativecommons.org/

licenses/by/4.0/).

Article

Data Storytelling and Decision-Making in Seaport Operations:

A New Approach Based on Business Intelligence

Marco Gonçalves

, Cátia Salgado

, Amaro de Sousa

and Leonor Teixeira

1,4,

Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), University of Aveiro,

3810-193 Aveiro, Portugal; marcofgoncalves@ua.pt

Administração dos Portos de Sines e do Algarve (APS), 7521-953 Sines, Portugal;

catia.salgado@apsinesalgarve.pt

Institute of Telecommunications—Pole of Aveiro (IT), Department of Electronics, Telecommunications and

Informatics (DETI), University of Aveiro, 3810-193 Aveiro, Portugal; asou@ua.pt

Intelligent Systems Associate Laboratory (LASI), Institute of Electronics and Informatics Engineering of

Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal

* Correspondence: lteixeira@ua.pt; Tel.: +351-234370361

Abstract: Seaports are experiencing several challenges due to the explosive growth of the

maritime shipping business, which has led to the need for digitalized operations and more

effective solutions. This article provides a comprehensive exploration of the process used to

create a reliable business intelligence solution by analyzing the container delivery and pick-

up services ﬂow in one of Portugal’s largest maritime container ports, using the CRISP-DM

methodology. The solution, built with Microsoft Power BI

, provides the capability to

identify and address data anomalies and present key performance indicators in visually

dynamic dashboards. This solution empowers stakeholders to gain invaluable insights into

the current and future operational status, thereby facilitating well-informed and adaptable

decision-making, representing the main practical contributions. As a theoretical contri-

bution, this study advances research by covering a gap in the literature and establishing

the foundations for future business intelligence applications within the maritime industry,

with a focus on addressing data dispersion challenges, enhancing logistics ﬂow analysis,

and reducing port congestion. The manuscript is structured into seven sections: introduc-

tion, literature review, port challenges, methodology, tool development, SWOT analysis,

and conclusion.

Keywords: business intelligence; performance indicators; dashboard; CRISP-DM; seaports

1. Introduction

Maritime transport is a critical component of the global economy, facilitating the

movement of 12 billion tons of goods in 2022 [

]. A signiﬁcant portion of these goods is

transported in containers, which offer a versatile and efﬁcient means of moving products

across various modes of transportation, such as ships, trucks, and trains. The demand for

container transport is expected to grow, with a projected annual increase of 3% in container

volume from 2024 to 2028 [2].

This growing reliance on maritime transportation comes with its own set of challenges,

particularly in terms of port logistics. Efﬁcient operations at container terminals are essential

to maintain scheduled services and minimize disruptions [

]. Issues, such as peak-hour

trafﬁc and delays, require the development of systems capable of predicting and addressing

these challenges [

]. While some ports have implemented truck scheduling systems to

Sustainability 2025, 17, 337 https://doi.org/10.3390/su17010337

Sustainability 2025, 17, 337

2 of 26

reduce waiting times and emissions, there remains a pressing need for more comprehensive

solutions to address the logistical complexities at terminal entrances [5].

In response to these challenges, the concept of smart ports has gained momentum,

incorporating Industry 4.0 principles and digitalization to transform supply chain man-

agement. Smart port initiatives utilize advanced technologies to enable large-scale data

analysis and process automation, boosting operational efﬁciency and resilience [

]. Issa

Zadeh et al. [

] deﬁnes smart ports through three key sectors: intelligent logistics, intelli-

gent infrastructure, and intelligent trafﬁc. These sectors include technologies such as IoT,

platforms, sensors, intelligent cargo handling, and energy management systems. When

these components are effectively integrated, they optimize port operations, enabling them

to meet growing demands with greater efﬁciency.

Despite recognizing the need for innovation, many ports still rely on manual solu-

tions that lead to inefﬁciencies [

]. Modernizing ports through digitalization is essential,

requiring the integration of systems and collaboration among stakeholders [

]. This trans-

formation involves creating infrastructure for real-time data collection and processing,

leveraging business intelligence tools to enhance planning and control of port opera-

tions [

]. Additionally, intelligent network technologies—such as IoT, information and

communication technology (ICT), and smart energy systems—play a critical role in im-

proving energy efﬁciency, sustainability, and the overall performance of seaports. These

technologies enable secure communication, real-time data processing, and effective energy

management, supporting the sustainable and efﬁcient operation of smart ports [10].

International organizations, such as the United Nations Conference on Trade and

Development (UNCTAD), advocate for increased investment in digitalization to enhance

port efﬁciency and resilience. Furthermore, governments are urged to drive reforms in port

infrastructure and operations to facilitate the adoption of digital solutions [11].

This research aims to drive the digital and ecological transition in transport, logistics,

and port operations by introducing a decision support tool developed using Microsoft

Power BI. While Power BI itself may not be a novel technology, the innovation in this

research lies in how it is applied to integrate and analyze fragmented data from port oper-

ations, providing actionable insights that directly inform decision-making and improve

operational efﬁciency. By addressing challenges, such as data dispersion, enhancing logis-

tics ﬂow analysis, and reducing port congestion, this investigation presents a compelling

case for the widespread application of business intelligence in the maritime sector, em-

phasizing its positive impact on port management and operations. For this purpose, the

application of the case took place in one of the largest container maritime ports in Portugal.

This paper is structured as follows: Section 2 reviews digital transformation in port lo-

gistics, focusing on data-driven decision-making and BI’s impact on data analysis.

Section 3

outlines the logistics processes at the terminal, highlighting container handling, service

scheduling challenges, and the need for the Power BI tool. Section 4 describes the devel-

opment methodology. Section 5 details the BI tool’s design, covering requirements, data

models, and dashboards. Section 6 evaluates the tool with a SWOT analysis. Section 7

summarizes the paper’s contribution and future research directions.

2. Literature Review

2.1. Digital Transition in Port Logistics Operations

The history of seaports can be divided into ﬁve generations, each with signiﬁcant

technological advances and operational changes. Similarly, many forms of digitalization

have been implemented in ports, amounting to three generations of digitization over the

ﬁve generations of ports [1].

Sustainability 2025, 17, 337

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Before 1950, the ﬁrst generation mainly involved manual port operations, such as

cargo handling and paper-based unloading procedures. Over time, these procedures

shifted to electronic formats through electronic data interchange (EDI), improving data

exchange and processing [

]. The second generation, until 1980, transformed ports into

centers of value-added services. This era saw increased raw material handling and the

emergence of the ﬁrst port communication systems (PCS), which improved coordination

and efﬁciency in port operations [

]. Starting in 1980, the third generation saw the in-

creasing importance of intermodal transport. Information technologies aimed at enhancing

operational efﬁciency were introduced, with ports adopting terminal operating systems

(TOS) to integrate data from various technologies and subsystems for more cohesive and ef-

fective

management [11,13]

. The fourth generation, from 1990, brought innovation through

the integration of port companies into common administrations and the development of

automated technologies, which are crucial for improving efﬁciency and automation in

terminal operations [

]. The ﬁfth generation, from 2010, introduced concepts such as

“Smart Ports”, “Industry 4.0”, “Digitalization”, and “Sustainability”. Ports began using

smart devices and mobile applications to optimize trafﬁc and cargo ﬂows. The integration

of various control centers into a centralized system aims to allow for real-time data analysis,

transforming how ports operate and adapt to modern needs [14].

Each generation brought important advancements, shaping modern ports and prepar-

ing them to meet the challenges and opportunities of the current global economy. The key

aspects of each generation of port digitalization are summarized in Table 1, illustrating the

continuous evolution and increasing complexity of port operations.

Table 1. Core elements of each port and digitalization generation, based on [12].

Port

Generation

Digitalization

Generation

Technology Purpose Contribution Obstacle

First

(Before 1960)

Second

(1960–1980)

Third

(1980–1990)

First

(1950–1990)

EDI, PCS, TOS

- Transformation

of all information

exchanges

between the

various

companies using

the port from

physical to

electronic

methods

- Better planning

of process

execution due to

the availability

of information

- Low adherence

of the port

community to

the systems

created, as

companies do

not abdicate

from using

physical

documents

Fourth

(1990–2010)

Second

(1990–2010)

RFID, TAS, Laser

- Automation of

terminal

activities

- Increased

efﬁciency and

operational

capacity

- Collected

information is

static

- Low adoption

of the truck

appointment

system (TAS)

Fifth

(After 2010)

Third

(since 2010)

Mobile Apps,

Cloud, Smart

devices

- Creation of

intelligent

processes that

integrate port

activity

- Better

coordination and

monitoring of

port activity

- Inefﬁciencies in

information ﬂow

- A certain

reluctance

among

stakeholders to

use disruptive

processes

Sustainability 2025, 17, 337

4 of 26

2.2. Decision-Making and the Potential of Data in Seaports

Decision-making is the cognitive process of reasoning used to select an action from

among several alternatives, which is considered crucial in organizations because it has a

decisive inﬂuence on their ability to adapt to change [15].

Ports generate a variety of data types, including logistical data, operational statistics,

cargo and shipping information, as well as environmental and security data. Business

intelligence tools play a crucial role in transforming this diverse and voluminous data into

actionable insights. These tools employ processes such as extract-transform-load (ETL) and

data virtualization to integrate data from disparate sources, ensuring that the information

is accurate and accessible for analysis [

]. Advanced analytics and AI-driven insights,

as part of BI, enable predictive capabilities, providing foresight into future trends and

potential issues, which is essential for the dynamic environment of ports [17].

The potential of data in seaports is immense, as it supports the evolution of these

key hubs in global trade networks into intelligent, efﬁcient, and sustainable operations.

Technologies such as the seaport data space (SDS) and big data architectures allow for

secure data sharing and improved decision-making through key performance indicators

(KPIs) displayed on dashboards. This integration can lead to decreased transaction costs

and enhanced operational quality [

]. Additionally, integrating port community systems

(PCS) is vital for the competitiveness of seaports, as it promotes coordinated communication

among stakeholders and boosts operational efﬁciency [19].

Moreover, the role of data extends beyond the seaports themselves to their intercon-

nections with inland freight distribution systems, such as dry ports. Research indicates that

operations at dry ports can impact seaport competitiveness by improving performance,

service variety, and capacity [20].

The increasing focus on data-driven decision-making in seaports is highlighted by [

who discussed the role of digital twins in facilitating transparent and controlled seaport

operations. Similarly, [22] emphasized the importance of advanced technologies and digi-

talization in optimizing seaport management. Furthermore, [

] underscored the impact of

dry port operations on seaport competitiveness, suggesting that seamless integration can

enhance overall performance and capacity.

Finally, [

] emphasized the importance of operational performance indicators and

the optimization of logistics networks for enhancing seaport efﬁciency.

Decision-making must be efﬁcient and effective, and data plays a key role in providing

information to identify problems, analyze alternatives, and ﬁnd solutions [24].

2.3. Business Intelligence—Beneﬁts and Applications to Seaports

The concept of business intelligence (BI) is the discipline that integrates infrastructure

to select, extract, process, and visualize data, enabling organizations to make data-driven

decisions [25].

BI has the potential to enhance decision-making in seaports by enabling the integration

and analysis of diverse data sources. This can result in improved operational efﬁciency and

better strategic planning [

]. Through data visualization and reporting tools, shipping data,

cargo movements, and vessel tracking are presented in a clear and actionable manner [

Predictive analytics utilizes historical data to forecast cargo volumes, enabling ports to

allocate resources effectively and prepare for future demands [

]. Monitoring operational

performance through KPIs helps evaluate and improve the efﬁciency of port operations [

Furthermore, BI applications facilitate supply chain optimization by enhancing stakeholder

collaboration through effective data sharing, ultimately leading to more streamlined and

responsive supply chain management [30].

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5 of 26

Several authors highlight the diverse beneﬁts of BI in the maritime sector. For in-

stance, the integration of BI with X-ray scanning technology has been proposed to improve

maritime security by providing enhanced visibility in supply chain operations [

]. Ad-

ditionally, the digitalization and automation of seaport infrastructures, supported by BI,

contribute to the optimization of goods and people management, as well as facilitating

a green energy transition [

]. Contradictions or interesting facts emerge when consider-

ing the varying stages of BI implementation across different seaports. While some ports,

like those in Croatia, are in the developmental phase, focusing on simplifying business

processes and stakeholder connectivity [

], others, such as those in China, have achieved

notable advancements in automation and intelligence, but still face challenges in fully

leveraging BI for business operations and decisions [33].

Digitalization is essential for the future of ports, as demonstrated by [

], who show

that ports with smart energy infrastructures can signiﬁcantly reduce carbon emissions

and improve operational efﬁciency, while ports that do not adopt such solutions face

inefﬁciencies and high environmental [

]. Moreover, the implementation of business

intelligence (BI) enables ports to make quick decisions based on real-time data, optimizing

logistics ﬂows and reducing their environmental impact. In contrast, ports that do not

adopt BI continue to operate with outdated data, resulting in operational inefﬁciencies

and greater environmental impacts [

]. Therefore, digitalization is an urgent necessity to

ensure the sustainability and competitiveness of ports in the future.

Ain et al. [

] highlight that BI systems offer a ﬂexible technological solution for

accessing data from multiple sources, enabling the accumulation, integration, and analysis

of data to identify opportunities, strengths, and weaknesses. These systems support

decision-making by facilitating advanced integration and management of structured and

unstructured data, handling large volumes of data, empowering end users with enhanced

processing capabilities to derive new insights, and providing solutions for ad hoc analysis

and queries.

BI applications encompass data mining techniques, data visualization, and perfor-

mance analysis, collectively converting raw data into valuable information. This trans-

formation is achieved through the development of dashboards, which act as an interface

between the system and users, visually summarizing performance indicators [

]. However,

Kruglov et al. [

] argue that dashboard development and associated indicators should be

customized to the end user, as one of the primary reasons for the rejection of this tool is the

lack of information that users genuinely need.

I¸sık et al. [

] underscore the potential of BI systems to manage varying volumes of

data generated at an increasing velocity due to business activities, combined with ongoing

market changes, prompting companies to prioritize their implementation. This prioritiza-

tion is motivated by their aim to differentiate themselves from competitors through more

precise decision-making.

3. Initial Status and Challenges of the Port Under Study

In the port under study, containers are associated with either import or export ﬂows.

For exports, the supplier dispatches the container via a hauler, and the terminal operator

manages its shipment and dispatch to the destination port. For imports, the container

arrives via maritime transport, and the terminal operator coordinates unloading and

delivery to a hauler for ﬁnal customer transport.

The transportation process begins when a hauler receives a request from the exporter

or importer. To enter the terminal, both the driver and the truck must be registered and

authorized in the system. The hauler must then schedule a 1-h time window for logistical

services (delivery, pickup, or both). For example, if a delivery is scheduled from 8:00 AM

Sustainability 2025, 17, 337

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to 9:00 AM, the driver must arrive within that timeframe, and the terminal operator must

be prepared to execute the service.

Service scheduling can be done in advance using the JUL system or in person at

the terminal’s kiosk. Advance scheduling is preferred as it automatically validates the

driver’s entry authorization. In-person scheduling requires manual data entry into the

GTOS system, which integrates with JUL.

There are two systems for recording data on container services: JUL, the port’s global

information system, which centralizes all activities, and GTOS, the terminal’s operational

system, which supports planning, execution, and control. JUL tracks appointments through

various stages: “Pending” (awaiting validation), “Scheduled” (after validation), “Running”

(service in progress), and “Realized” (completed). Other statuses include “Not performed”

(service not started) and “Canceled” (due to hauler or terminal operator issues or expiry).

As container volumes increase, terminal operations face signiﬁcant pressure, exposing

inefﬁciencies. The main issue is the disorganization caused by a lack of demand forecasting

and the terminal operator’s inability to adapt resources effectively. The JUL system’s

inefﬁciency, due to low hauler adoption and unexpected cancellations, leads to truck

queues and congestion at the port entrance.

Despite limited adherence to the scheduling platform, it remains useful for quantifying

scheduled services. The port authority is focused on improving processes to address these

inefﬁciencies and better manage the scheduling and execution of logistics services.

Technological advancements have led to the creation of isolated “data islands”, with

logistics data fragmented within JUL, resulting in integration challenges, particularly with

GTOS. This lack of data consolidation leads to inefﬁcient information ﬂows, which hinders

timely access to critical operational insights.

This work aims to develop a Power BI tool that extracts and transforms data from

logistics services, providing insights that support decision-making and mitigate current in-

efﬁciencies. The BI system will aggregate data from various sources into a single repository,

presenting relevant information in a user-friendly, graphical format to improve accessibility

and understanding across the organization.

4. Development Methodology

The development of the BI tool has followed the cross-industry standard process

for data mining methodology (Figure 1), comprising six main phases. This methodology

guarantees a systematic and effective resolution of the challenges identiﬁed, although it

is not able to fully integrate data from both systems. It is, therefore, a starting point for

obtaining agile information to support decision-making.

Sustainability 2025, 17, x FOR PEER REVIEW 7 of 28

Service scheduling can be done in advance using the JUL system or in person at the

terminal’s kiosk. Advance scheduling is preferred as it automatically validates the driver’s

entry authorization. In-person scheduling requires manual data entry into the GTOS

system, which integrates with JUL.

There are two systems for recording data on container services: JUL, the port’s global

information system, which centralizes all activities, and GTOS, the terminal’s operational

system, which supports planning, execution, and control. JUL tracks appointments

through various stages: “Pending” (awaiting validation), “Scheduled” (after validation),

“Running” (service in progress), and “Realized” (completed). Other statuses include “Not

performed” (service not started) and “Canceled” (due to hauler or terminal operator

issues or expiry).

As container volumes increase, terminal operations face signiﬁcant pressure,

exposing ineﬃciencies. The main issue is the disorganization caused by a lack of demand

forecasting and the terminal operator’s inability to adapt resources eﬀectively. The JUL

system’s ineﬃciency, due to low hauler adoption and unexpected cancellations, leads to

truck queues and congestion at the port entrance.

Despite limited adherence to the scheduling platform, it remains useful for

quantifying scheduled services. The port authority is focused on improving processes to

address these ineﬃciencies and beer manage the scheduling and execution of logistics

services.

Technological advancements have led to the creation of isolated “data islands”, with

logistics data fragmented within JUL, resulting in integration challenges, particularly with

GTOS. This lack of data consolidation leads to ineﬃcient information ﬂows, which hinders

timely access to critical operational insights.

This work aims to develop a Power BI tool that extracts and transforms data from

logistics services, providing insights that support decision-making and mitigate current

ineﬃciencies. The BI system will aggregate data from various sources into a single

repository, presenting relevant information in a user-friendly, graphical format to

improve accessibility and understanding across the organization.

4. Development Methodology

The development of the BI tool has followed the cross-industry standard process for

data mining methodology (Figure 1), comprising six main phases. This methodology

guarantees a systematic and eﬀective resolution of the challenges identiﬁed, although it is

not able to fully integrate data from both systems. It is, therefore, a starting point for

obtaining agile information to support decision-making.

Figure 1. Diagram of the methodology adopted based on the CRISP-DM cycle.

Sustainability 2025, 17, 337

7 of 26

As discussed in the previous chapters, the need for this BI solution arises from the

investment in the digitalization of ports as a strategy to address the growth in maritime

transport and mitigate the lack of agile information that compromises the effectiveness of

port services. It is important to note that there are not many BI applications speciﬁcally

targeted at this sector. While the CRISP-DM methodology is not typically considered

agile, it was chosen for this study due to its structured approach, which provided a clear

and systematic framework for addressing the challenges in port operations. The dynamic

and complex nature of seaport environments requires a method that can both guide and

adapt to evolving needs. Despite its more rigid structure, CRISP-DM was implemented to

ensure a coherent process for data collection, transformation, and analysis. This approach

enabled the identiﬁcation and resolution of critical data fragmentation issues, contributing

to better-informed decision-making.

During the early stages of the project, several meetings were held to understand how

the port operates. The main goal was to identify the current issues and ﬁnd opportunities

to improve data analysis and decision-making procedures.

The second phase focused on identifying the key data sources for analysis. The main

sources identiﬁed were the scheduling system “JUL”, which handles appointments, and

the operational system “GTOS”, which manages operations at the terminal. A thorough

evaluation of the data’s quality was conducted to ensure completeness, accuracy, timeliness,

consistency, and accessibility. Data integrity checks were performed to ensure consistent

formatting, completeness, and the absence of null or duplicate records. Any discrepancies

found were noted for future resolution in the source systems and the BI system.

In the third phase, the focus shifted to addressing inconsistencies in the data. This

involved data cleansing and the application of data mining techniques such as classiﬁcation

and regression to create additional variables. A composite key was developed to cross-

reference the data from the two systems, expanding analytical possibilities. Following

this approach, ﬁnal attributes and records were selected for loading into the BI system’s

data warehouse. KPIs, which are deemed necessary by stakeholders, were established to

provide a comprehensive picture of scheduling and operational performance at the port.

Using the Kimball technique, which employs dimensional modeling to depict rela-

tionships between data tables, the fourth phase involved building the data warehouse.

Snowﬂake and constellation schemas were used to organize the data effectively. Subse-

quently, dashboards featuring key performance indicators were created with a visually

appealing and user-friendly interface to simplify data interpretation.

In the ﬁfth phase, a comprehensive SWOT analysis was performed to assess the

effectiveness of the proposed solution, with a particular focus on the dashboards, since

they are the interface between the ﬁnal solution and the end users. The evaluation aimed

to ascertain the tool’s ability to robustly and efﬁciently analyze the terminal’s systems.

The sixth and ﬁnal phase involved preparing the solution for implementation to

analyze the operations of the terminal under study, which is currently under evaluation

by the port authority. A ﬁnal report that included a detailed account of every phase of the

development process was also produced. The goal of this documentation is to demonstrate

the solution’s ﬁnished product and facilitate its duplication or environment adaptation,

therefore guaranteeing the solution’s ongoing improvement.

The process outputs involve delivering a functional business intelligence tool. This

technology has been actively employed at the speciﬁc port being studied to carry out a

sample analysis of the existing logistics operations and to assess its impact.

Sustainability 2025, 17, 337

8 of 26

5. BI Tool: Features and Practical Outcomes

This section outlines the key ﬁndings related to the conceptualization and development

of the business intelligence tool. The tool comprises three different modules, referred to in

this paper as applications: one for data collected from the scheduling system (referred to as

JUL), another for data collected from the operator’s system coordinating terminal activities

(referred to as GTOS), and a third for cross-referencing data from both systems (referred

to as INT). Evaluating new parameters, such as the percentage of previously scheduled

services and compliance with time windows, was only possible by cross-checking the data

in the INT application.

This modular approach was chosen to allow separate analysis of the systems, which

are not fully integrated and are operated by different entities. Developing the tool as a single

application would compromise its integrity in the event of errors related to cross-sampling

data and decrease its query performance.

5.1. Data Preparation

After importing the data from the systems, a quality study was undertaken to identify

the non-conformities in the data according to ﬁve dimensions: completeness, accuracy,

timeliness, consistency and accessibility. The solutions adopted for the non-conformities

detected are presented in Table 2 according to their purpose: cleaning; exclusion.

To simplify and guide the interpretation of the analyses, variables were created from

the initial attributes of the datasets.

The approach adopted was mainly based on constructing variables from attributes,

mainly of the datetime type, using the regression technique and, where appropriate, classi-

fying them into categories.

An example of this approach is the construction of the continuous variable “Appoint-

ment Advance” (Table 3), based on the difference between the start date of the time slot and

the creation date of the appointment, where each value was classiﬁed using the variable

“Creation Time”. For example, when the “Appointment Advance” has a negative value,

then the value shown by the “Creation Time” variable is ‘Created during the time window’.

This type of approach not only improves understanding of the values, but also makes it

easier to compare them with other attributes, enriching the scope of the analysis.

Table 3 shows the variables constructed in the JUL dataset where “Delay End” and

“End Type” follow the same approach. In order to simplify attributes that gave similar

information, they were merged into one, as is the case with “Proﬁle_Method”, where the

union of the “Proﬁle” and “Method” attributes was considered relevant because drivers

make exclusive use of the mobile application, while the carrier only uses the website, and

null values always correspond to the integration. One of the crucial attributes for further

analysis, the “Time Window”, was created from the respective “Start” and “End” attributes.

However, before joining these attributes, and to preserve the date provided by them, the

“Service Date” variable was created.

Sustainability 2025, 17, 337

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Table 2. Main problems and solutions adopted in data preparation.

Dimension Problem Solution Adopted

Completeness

JUL data does not include in-person kiosk

bookings, limiting the overall view of service

ﬂows.

Complementary analysis using GTOS data to

characterize general service ﬂows.

GTOS data contains limited attributes to detail

services and time slots.

Integration of JUL data and creation of a

composite key to cross-reference information

between the systems.

Accuracy

Problem 1: Incorrect bookings for booking and

bill of lading (service type and container

movement). (7283 occurrences in

~80,000 records)

Cleansing: Substitution of incorrect values in

attributes, adjusting to ‘Import’ and ‘Pick-up’

as required.

Problem 2: Delivery bookings associated with

import movements.

(1 occurrence in ~80,000 records)

Cleansing: Correction of the “Container

Movement” attribute to “Export”.

Problem 3: Bookings marked as “Completed”

with cancellation reasons.

(706 occurrences in ~80,000 records)

Cleansing: Adjustment of the “Status” attribute

to ‘Cancelled’ for applicable cases.

Problem 4: Duplicate values in cancellation

reasons.

(1265 occurrences in ~80,000 records)

Cleansing: Replacement of ‘Rejected by

Terminal Operator’ with ‘Cancelled by

Terminal Operator’.

Problem 5: Times in UTC time zone (no

adjustment for Portugal’s summertime).

(All records with time data)

Cleansing: Conversion of all timestamps to

Portugal’s time zone using a custom function.

Consistency

Problem 6: Missing booking IDs in multiple

records.

Exclusion: Removal of these records

Problem 7: Missing timestamps for booking

evolution in 73% of records.

(58,650 occurrences in ~80,000 records)

Exclusion: Removal of the booking timestamp

attributes

Problem 8: Missing end-of-time-slot dates in

some records.

(25 occurrences in ~80,000 records)

Exclusion: Removal of these records

Problem 9: Delivery and pick-up bookings for

the same container with inconsistent statuses.

(9 occurrences in ~80,000 records)

Exclusion: Removal of these records

Problem 10: Inconsistent formats in truck and

container license plates.

Cleansing: Removal of non-alphanumeric

characters from the respective attributes using

a custom function

Problem 11: Inconsistent formats in transport

company names.

Cleansing: Manual mapping to align names

between JUL and GTOS; development of an

auxiliary function for standardization.

Timeliness

The period of the JUL dataset is between November 2021 and February 2024, and the GTOS

dataset is between January 2022 and December 2023, so the data are up to date in time, where the

ease of future updates is related to the use of the same SQL query and its accessibility.

Accessibility

The process of extracting the data was carried out using an SQL query by specialized technicians

from the respective systems. The query generated Excel ﬁles that served as the starting point for

building the proposed BI tool. Therefore, it is not possible to assess the accessibility of the data,

and it is important to note that future updates of the tool and the dataset will require the use of the

same SQL query or, alternatively, adjustments based on the logic explained throughout the work.

GTOS

(speciﬁc)

Problem 12: Outliers in service duration.

Implementation of mechanisms to detect and

exclude outliers, while allowing users the

option to manually analyze them.

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Table 3. Variables created in the JUL dataset.

Variable Name Description Type

Appointment Advance

Difference in minutes between the start of the scheduled time window

and the creation time.

Double

Creation Time

Classiﬁcation of Appointment Advance: ‘Before Window’, ‘During

Window’, ‘During Integration’, or ‘After Window’.

String

Proﬁle_Method

Combination of “Proﬁle” and “Method”: ‘Web-Transporter’,

‘App-Driver’, ‘Integration’.

String

Service Date Date for which the service was scheduled. Date

Time Window

Concatenation of the start and end times of the time window in a

simpliﬁed format, e.g., ‘16:00–17:00’.

String

Delay End

Difference in minutes between the recorded completion timestamp

and the end of the time window.

Double

End Type

Classiﬁcation of how the scheduling was completed: ‘Not Recorded’,

‘Cancelled’, ‘Auto Closed’, ‘Late Close’, ‘On-Time Close’.

String

Table 4 shows the variables created in the GTOS dataset. Initially, due to the lack of

a unique identiﬁer for each service, the “Service ID” was introduced. Since there was no

attribute in this dataset that described the “Type of Service” according to the speciﬁcation

presented, this variable was constructed by classifying combinations of the attributes

“Loads”, “Unloads”, and “Type of Journey”. For example, a double trip corresponds to a

delivery and lifting service, while if it is a single trip and there is a record of an unloaded

container, it is a delivery, and the other possible case corresponds to the lifting of a container.

To analyze the duration of the services and gauge the performance of the terminal operator

in completing them, the variable “Service Duration” was constructed from the difference

between the time the truck left and entered the terminal. Considering the lack of context

about the time windows marked in this dataset, the variables “Entry Time Window” and

“Exit Time Window” were created to group truck entries and exits into one-hour intervals

throughout the day, as stipulated in the terminal operator’s schedule. Before creating

these variables and to preserve the date present in them, the “Service Date” was entered.

Ideally, a service should be completed within the same time window in which the truck

entered the terminal. To assess this compliance, the “Time Offset” variable was introduced,

which represents the difference, in hours, between the truck’s departure and entry time.

Therefore, this metric quantiﬁes the number of time windows that elapsed between the

truck’s departure and entry of the truck at the terminal.

Table 4. Variables created in the GTOS dataset.

Variable Name Description Type

Service ID Unique identiﬁer for each service. Integer

Service Date Datetime when the truck entered the terminal for the service. Date

Service Type Classiﬁcation of the service: ‘Delivery’, ‘Pickup’, or ‘Delivery and Pickup’. String

Service Duration Duration in minutes from the truck’s terminal entry to its exit. Double

Entry Time Window 1-h time slot during which the truck entered the terminal. String

Exit Time Window 1-h time slot during which the truck exited the terminal. String

Time Offset Difference in hours between the time windows of truck exit and entry. Integer

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Mechanisms for identifying outliers were also added. Based on the interquartile range

(IQR) technique, a method was developed to detect univariate outliers, shown in Figure 2

with the example of the variable “Average Service Duration”, which corresponds to GTOS

problem 12 identiﬁed in the data quality survey (Table 2, last row). The IQR method,

intrinsic to the box-and-whisker plot, was chosen for this work as it is a simple, robust, and

widely used technique from a general practical perspective, with no speciﬁc assumptions

about the data distribution. Outliers identiﬁed by the method are excluded by default, but

users have the option to analyze them further if required. However, this method can be

applied in an equivalent way to other variables, depending on requirements.

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Figure 2. Outlier identiﬁcation method.

Table 5 summarizes the evolution of the aributes during data processing. Data

preparation resulted in the exclusion of 5663 records in JUL and the standardization of the

aributes in both datasets. In addition, seven variables were created in JUL and seven in

GTOS, which will play a crucial role in the analyses. Finally, 16 aributes in JUL and 3 in

GTOS that did not add value were excluded. Thus, 19 aributes were selected for the ﬁnal

JUL model and 11 aributes for the ﬁnal GTOS model.

Table 5. Evolution of the number of aributes and records throughout data processing.

System

No. Initial

Aributes

No. Initial

Records

No. Final

Aributes

No. Final

Records

JUL 27 100.348 19 94.685

GTOS 7 111.984 11 111.984

5.2. Requirements and Indicators for the BI Tool

The requirements were raised with the stakeholders, considering the context of the

seaport under study and the data available for collection from existing systems. The

iterative process to deﬁne the ﬁnal key performance indicators (KPIs) involved multiple

stages. Initially, a comprehensive set of potential KPIs was identiﬁed, based on the raw

data extracted from the operational systems. This preliminary list was evaluated in a

series of meetings with stakeholders, where the relevance and applicability of each KPI

were discussed in detail.

These discussions revealed that certain indicators were less critical or redundant and

were subsequently excluded. Conversely, priority was given to the indicators that directly

addressed identiﬁed monitoring needs, such as operational adjustments, resource

optimization, and strategic decision-making. Ultimately, 14 KPIs were selected from the

initial set, as they most eﬀectively reﬂected the speciﬁc requirements of each user proﬁle.

The relevant user proﬁles have been identiﬁed (“terminal operators”, “port

administrations”, “haulers and drivers”) with dedicated dashboards. Dashboards were

implemented for haulers and their drivers to display the logistics operator’s capacity

availability for various time windows and days of the week. Terminal operators needed

dashboards to showcase the most requested containers, periods of highest demand, and

the haulers with the highest number of appointments, to adjust their resources and

prevent service disruptions. For port administrations, the dashboards were required to

present summary performance indicators such as the percentage of prior appointments

and the time in advance they were conducted. In the laer case, the analysis provided by

the dashboards is aimed to support the implementation of strategic port policies (for

example, the implementation of mandatory prior scheduling).

Figure 2. Outlier identiﬁcation method.

Table 5 summarizes the evolution of the attributes during data processing. Data

preparation resulted in the exclusion of 5663 records in JUL and the standardization of the

attributes in both datasets. In addition, seven variables were created in JUL and seven in

GTOS, which will play a crucial role in the analyses. Finally, 16 attributes in JUL and 3 in

GTOS that did not add value were excluded. Thus, 19 attributes were selected for the ﬁnal

JUL model and 11 attributes for the ﬁnal GTOS model.

Table 5. Evolution of the number of attributes and records throughout data processing.

System

No. Initial

Attributes

No. Initial

Records

No. Final

Attributes

No. Final

Records

JUL 27 100.348 19 94.685

GTOS 7 111.984 11 111.984

5.2. Requirements and Indicators for the BI Tool

The requirements were raised with the stakeholders, considering the context of the

seaport under study and the data available for collection from existing systems. The

iterative process to deﬁne the ﬁnal key performance indicators (KPIs) involved multiple

stages. Initially, a comprehensive set of potential KPIs was identiﬁed, based on the raw

data extracted from the operational systems. This preliminary list was evaluated in a series

of meetings with stakeholders, where the relevance and applicability of each KPI were

discussed in detail.

These discussions revealed that certain indicators were less critical or redundant

and were subsequently excluded. Conversely, priority was given to the indicators that

directly addressed identiﬁed monitoring needs, such as operational adjustments, resource

optimization, and strategic decision-making. Ultimately, 14 KPIs were selected from the

initial set, as they most effectively reﬂected the speciﬁc requirements of each user proﬁle.

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The relevant user proﬁles have been identiﬁed (“terminal operators”, “port administra-

tions”, “haulers and drivers”) with dedicated dashboards. Dashboards were implemented

for haulers and their drivers to display the logistics operator’s capacity availability for

various time windows and days of the week. Terminal operators needed dashboards

to showcase the most requested containers, periods of highest demand, and the haulers

with the highest number of appointments, to adjust their resources and prevent service

disruptions. For port administrations, the dashboards were required to present summary

performance indicators such as the percentage of prior appointments and the time in ad-

vance they were conducted. In the latter case, the analysis provided by the dashboards is

aimed to support the implementation of strategic port policies (for example, the implemen-

tation of mandatory prior scheduling).

A consistent requirement across all user proﬁles was the need for the tool to be

user-friendly, with information presented clearly and concisely using appropriate graphs

and diagrams. The selected indicators were also designed to be analyzed from three

perspectives: daily trends, overall performance, and contribution to the total, offering

ﬂexibility in monitoring operations.

Out of 30 ﬁnal attributes selected in the quality treatment (the sum of the fourth

column values, Table 5), 14 KPIs were established and implemented using Power BI’s data

analysis expressions (DAX) language (Table 6). The indicators can be ﬁltered by context,

enabling diversiﬁed analysis without modifying their structure.

Table 6. KPIs deﬁned and their description.

KPI Description Application

No. Appointments Unique count of prior appointments in the JUL application.

JUL

% Cancellation

Percentage of canceled prior appointments, calculated by

dividing the number of canceled prior appointments by the

total number of prior appointments.

Average Appointment Advance

Average time (minutes) between appointment scheduling time

and the start of the scheduled time window.

Containers per Appointment

Total number of containers handled by each prior

appointment.

No. Services Unique count of services in the GTOS application.

GTOS

Average Duration of Service Average time (minutes) taken per service.

% Entry and Exit Same Time Window

Ratio between the number of trucks entering and leaving in

the same time window and the total number of trucks that

entered in the same time window.

Availability Time Window

Difference between the operator’s capacity and the number of

services per time window divided by the total number of

services.

Containers Delivered and Picked Up Total number of containers handled per service.

% Scheduled Services

Percentage of services scheduled in advance obtained by

dividing the number of previously scheduled services by the

total number of services.

INT

No. Scheduled Services Unique count of scheduled services in the INT application.

% Compliance

Percentage of services completed within the scheduled time

window obtained through the ratio between the number of

services that comply the time window and the total number of

services.

Average Entry Delay

Average time (minutes) between the start of the time window

and the time the truck entered the terminal

Average Exit Delay

Average time (minutes) between the end of the time window

and the time the truck exited the terminal

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The “No. Appointments” is the KPI for assessing the JUL’s performance. By quanti-

fying previous appointments, it is possible to monitor variations and identify trends that

allow for adjustments in strategy during peak demand periods. Additionally, knowing the

number of prior appointments is essential for measuring the adoption of this system by

haulers and their drivers, which is crucial for quantifying the current problem.

The “% Cancellation” KPI is a measure of the proportion of previous appointments

that have been canceled compared to the total number of prior appointments. This metric

is signiﬁcant as it highlights the effectiveness and reliability of JUL. A high cancellation

rate can indicate various issues. Difﬁcult-to-use systems might lead to scheduling errors,

causing terminal operators to cancel. Additionally, a lack of ﬂexibility could force haulers

to cancel. Poorly adjusted operational capacity might also prevent terminal operators from

meeting demand, leading to dissatisfaction among haulers. Therefore, maintaining a low

cancellation rate is essential for ensuring smooth operations and high hauler satisfaction.

The “Average Appointment Advance” KPI gives the average time between the creation

of the appointment and the start time window of the service. Monitoring these data is

essential to avoid problems with operational capacity and ﬂexibility. A shorter average

time in advance may signal imminent service overload, leading to challenging resource

management and delays. Conversely, a longer average time in advance enables more

efﬁcient operational planning, driving productivity gains.

The “Containers per Appointment” KPI shows the number of containers handled

in each previous appointment, which helps in making more accurate adjustments in the

allocation of resources. Also, the terminal operator’s income depends on the movement

of containers, so understanding the number of containers moved is crucial for managing

operating costs and income effectively.

The “No. of Services” is a metric that captures the total count of services registered in

GTOS. Unlike the “No. of Appointments”, which records previously scheduled appoint-

ments, this indicator quantiﬁes the actual provision of services at the port. It plays a crucial

role in identifying potential expansion opportunities or the need to adjust service types.

By analyzing this metric, terminal operators can identify areas with a growing demand

for speciﬁc services, enabling them to make informed investment decisions to better meet

customer needs and gain a larger market share.

The “Service Duration” KPI refers to the average time spent by the terminal operator

on each service. This measure is valuable for identifying variations in the level of service

provided to different haulers and/or at different times. Analyzing these data make it

possible to plan the number of services that can be carried out within a speciﬁc timeframe,

ensuring optimal and effective capacity utilization.

The “%Entry and Exit Same Time Window” KPI empowers terminal operators to

evaluate the efﬁciency of services using only the data from its system (GTOS), and it is

calculated as the ratio between the number of trucks entering and leaving in the same

time window and the total number of trucks that entered in the same time window. A

high value for this indicator implies effective coordination across different stages of the

logistics process, which is essential for ensuring seamless operations. On the other hand, a

low value may signal planning issues that result in delays, congestion, and dissatisfaction

among haulers.

The “Availability Time Window” KPI assesses the unused capacity in each period

through the difference between the operator’s capacity and the number of services per time

window divided by the total number of services. This indicator is essential for improving

resource utilization and planning. By identifying periods of idle capacity, the terminal can

reallocate resources, adjust the scheduling of operations to ﬁll these periods, and reduce

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costs associated with underutilization. At the same time, better management of available

capacity makes it possible to increase operational efﬁciency.

The “Containers Delivered and Picked Up” KPI counts all the containers handled at

the port. This metric provides a comprehensive count of the containers handled, unlike

the “Containers per Appointment” indicator, which only counts containers from previous

appointments. This helps in estimating the total operating costs and revenues related to

container handling, which is crucial for ensuring the proﬁtability of the business.

The “% Scheduled Services” is a KPI calculated by dividing the “No. of Appointments”

by the “No. of Services”, reﬂecting the percentage of services scheduled in advance at JUL.

The optimal target for this metric is close to 100 percent. Lower values may indicate a lack of

prior scheduling, while higher values could suggest missed appointments. Analyzing this

metric provides insight into hauler adoption of the scheduling system and helps evaluate

excessive demand or underutilization of resources.

The “No. of Scheduled Services” quantiﬁes the total number of records in the third

application (INT). Like the “No. of Services” and “No. of Appointments”, this indicator is

vital for assessing other indicators within this application.

The “%Compliance” KPI quantiﬁes the percentage of services that are fulﬁlled within

the scheduled time window, and is essential for assessing the haulers’ commitment to

comply with the agreed time window and the terminal operator’s ability to provide the

service as planned. High values for this indicator show that the services are fulﬁlled by

those involved within the established schedule, improving operational efﬁciency and effec-

tiveness. Therefore, this metric measures the punctuality of services, driving both internal

efﬁciency and hauler satisfaction, which is essential for the growth and sustainability of

the business.

The “Average Entry Delay” refers to the time difference between when the truck enters

the terminal and the start time of the time window. The “Average Exit Delay” refers to

the time difference between when the truck leaves the terminal and the end time of the

time window. Monitoring and reducing these delays contribute to hauler satisfaction by

providing a more predictable and reliable service.

The establishment of these requirements and the subsequent processing of the data

turned raw data into actionable information, as the indicators established make it possible

to measure the operational performance of seaports.

5.3. The BI Tool from the Perspective of Data Architecture and Technology

Regarding the architecture of the data model, the Kimball method was used due

to its suitability for the context and requirements identiﬁed. This method was the most

appropriate considering the data available, the lower complexity of the model, and the

shorter development time. Furthermore, this design offers a straightforward solution with

quick response times, increasing the tool’s usability for end users.

In the applications for the separate systems (Figure 3), the data model was constructed

in a uniform manner. It is important to note that only the date dimension (‘dCalendar’) was

normalized, as an analysis of the fact tables (‘fAppointments’, left of Figure 3 ‘fServices’,

right of Figure 3) revealed that other dimensions would typically have just one or two

attributes. This decision was based on prioritizing the performance of Power BI, which

can effectively handle simpler data models, thus avoiding unnecessary complexity from

adding dimensions with minimal attributes. The emphasis was placed on practicality and

efﬁciency in data analysis, maintaining a straightforward and easily interpretable model.

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Sustainability 2025, 17, x FOR PEER REVIEW 15 of 28

appointments. This helps in estimating the total operating costs and revenues related to

container handling, which is crucial for ensuring the proﬁtability of the business.

The “% Scheduled Services” is a KPI calculated by dividing the “No. of

Appointments” by the “No. of Services”, reﬂecting the percentage of services scheduled

in advance at JUL. The optimal target for this metric is close to 100 percent. Lower values

may indicate a lack of prior scheduling, while higher values could suggest missed

appointments. Analyzing this metric provides insight into hauler adoption of the

scheduling system and helps evaluate excessive demand or underutilization of resources.

The “No. of Scheduled Services” quantiﬁes the total number of records in the third

application (INT). Like the “No. of Services” and “No. of Appointments”, this indicator is

vital for assessing other indicators within this application.

The “%Compliance” KPI quantiﬁes the percentage of services that are fulﬁlled within

the scheduled time window, and is essential for assessing the haulers’ commitment to

comply with the agreed time window and the terminal operator’s ability to provide the

service as planned. High values for this indicator show that the services are fulﬁlled by

those involved within the established schedule, improving operational eﬃciency and

eﬀectiveness. Therefore, this metric measures the punctuality of services, driving both

internal eﬃciency and hauler satisfaction, which is essential for the growth and

sustainability of the business.

The “Average Entry Delay” refers to the time diﬀerence between when the truck

enters the terminal and the start time of the time window. The “Average Exit Delay” refers

to the time diﬀerence between when the truck leaves the terminal and the end time of the

time window. Monitoring and reducing these delays contribute to hauler satisfaction by

providing a more predictable and reliable service.

The establishment of these requirements and the subsequent processing of the data

turned raw data into actionable information, as the indicators established make it possible

to measure the operational performance of seaports.

5.3. The BI Tool from the Perspective of Data Architecture and Technology

Regarding the architecture of the data model, the Kimball method was used due to

its suitability for the context and requirements identiﬁed. This method was the most

appropriate considering the data available, the lower complexity of the model, and the

shorter development time. Furthermore, this design oﬀers a straightforward solution with

quick response times, increasing the tool’s usability for end users.

Figure 3. Data of the JUL application (left) and GTOS application (right).

In both models (Figure 3), the tables are linked using the “Date” aribute, which

serves as the primary key of the ‘dCalendar’ table. This means that an appointment or

service occurs exclusively on a date, which may correspond to a holiday at most,

indicating a snowﬂake schema. The fact that the ‘subdHoliday’ table is a subdimension of

the ‘dCalendar’ table further conﬁrms this schema.

Figure 3. Data of the JUL application (left) and GTOS application (right).

In both models (Figure 3), the tables are linked using the “Date” attribute, which

serves as the primary key of the ‘dCalendar’ table. This means that an appointment or

service occurs exclusively on a date, which may correspond to a holiday at most, indicating

a snowﬂake schema. The fact that the ‘subdHoliday’ table is a subdimension of the

‘dCalendar’ table further conﬁrms this schema.

The third application (INT) included the ‘dWindow’, ‘dTypeService’, and ‘dRoad-

Hauler’ dimensions. Each of these dimensions has a single attribute that acts as a primary

key: “Window”, “Type_Service”, and “Name_RH” (Figure 4). Despite the initial assump-

tion of avoiding the creation of dimensions with only one attribute, in this speciﬁc case,

this approach was followed given the presence of multiple fact tables and the need to ﬁlter

them simultaneously (‘fAppointments’ and ‘fServices’) to evaluate indicators such as “%

Scheduled Services”.

Sustainability 2025, 17, x FOR PEER REVIEW 16 of 28

The third application (INT) included the ‘dWindow’, ‘dTypeService’, and

‘dRoadHauler’ dimensions. Each of these dimensions has a single aribute that acts as a

primary key: “Window”, “Type_Service”, and “Name_RH” (Figure 4). Despite the initial

assumption of avoiding the creation of dimensions with only one aribute, in this speciﬁc

case, this approach was followed given the presence of multiple fact tables and the need

to ﬁlter them simultaneously (‘fAppointments’ and ‘fServices’) to evaluate indicators such

as “% Scheduled Services”.

Figure 4. Data model of the INT application.

Thus, there are three fact tables in the model, each ﬁltered by diﬀerent dimensions,

resulting in a constellation schema (Figure 4). The appointment (‘fAppointments’) and

service (‘fServices’) are each associated with only one type of service, hauler, time

window, and date. Additionally, the ‘fIntegration’ table contains records of where

appointments from ‘fAppointments’ and services from ‘fServices’ were matched. This

table is ﬁltered only by “Date”, following the approach used in the previous models

(Figure 3).

This modeling approach ensured eﬀective and eﬃcient data analysis, while

maintaining the simplicity and robustness of Power BI’s performance.

5.4. The BI Tool from the Perspective of Interaction with Users

The dashboards serve as the core deliverable of the BI tool and act as the primary

interface for engaging with end users. Customized dashboards were developed for each

application to meet the speciﬁc needs of the identiﬁed user proﬁles. Additionally, each

application features a starting menu designed to enable smooth navigation through the

range of dashboards, as illustrated in Figure 5.

Figure 4. Data model of the INT application.

Thus, there are three fact tables in the model, each ﬁltered by different dimensions,

resulting in a constellation schema (Figure 4). The appointment (‘fAppointments’) and

service (‘fServices’) are each associated with only one type of service, hauler, time window,

and date. Additionally, the ‘fIntegration’ table contains records of where appointments

from ‘fAppointments’ and services from ‘fServices’ were matched. This table is ﬁltered

only by “Date”, following the approach used in the previous models (Figure 3).

This modeling approach ensured effective and efﬁcient data analysis, while maintain-

ing the simplicity and robustness of Power BI’s performance.

5.4. The BI Tool from the Perspective of Interaction with Users

The dashboards serve as the core deliverable of the BI tool and act as the primary

interface for engaging with end users. Customized dashboards were developed for each

application to meet the speciﬁc needs of the identiﬁed user proﬁles. Additionally, each

Sustainability 2025, 17, 337

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application features a starting menu designed to enable smooth navigation through the

range of dashboards, as illustrated in Figure 5.

Sustainability 2025, 17, x FOR PEER REVIEW 17 of 28

Figure 5. Menu of the three applications (JUL top left, GTOS top right and INT boom).

In terms of structure and presentation, there are shared views and elements among

the diﬀerent applications, making it easier to use the tool across various systems for

parallel use and comparison of appointment volumes and services.

The applications share the following three views (Figure 5). The categories view is

helpful for terminal operator analysis because it allows comparison of the system’s

primary indicators across all categories. The time view supports the terminal operator in

spoing paerns and foreseeing potential issues by making it simpler to comprehend how

indications change over time. The summary view (available only in JUL and GTOS

analytics) provides a high-level overview that enables the port authority to eﬃciently

assess the terminal’s performance by summarizing the KPIs of the system.

Additionally, there are speciﬁc dashboards in each system, adapted to their

particularities: in JUL, there is a dashboard that covers only cancellations; in GTOS, there

is a dashboard that presents the availability of time windows; and in INT, a dashboard

that assesses compliance with the time window.

Several dashboards were developed, each prominently displaying the identiﬁcation

of the source application and the speciﬁc object being analyzed, providing a clear and

organized interface for users to make comparisons and evaluations. The ﬁve most relevant

dashboards, out of a total of ten developed, will be presented next.

The “Categories View” dashboard aims to provide a detailed and versatile analysis

of appointment data across diﬀerent categories. This dashboard is illustrated when used

in JUL analytics (Figure 6), and displays two indicators based on the selections made in

the left menus. The top graph shows the “Average Appointment Advance”, based on the

category chosen in the top left selection menu (this top graph also provides a color

gradient representation of the Number of Appointments). The boom graph shows the

“Number of Appointments”, again based on the category chosen in the top left selection

menu and on the selections made in the other two menus. The boom graph aims to

provide a comprehensive view of the distribution of the number of appointments across

diﬀerent values of the chosen category. These data can be further analyzed daily, as a

total, or as a percentage of the total, giving users versatile options for assessing and

understanding the appointment data.

This dashboard is important as it allows the proﬁle of the terminal operator to explore

the appointment data across various categories, uncovering unique trends and insights

speciﬁc to each category. By doing so, it helps in making more informed and targeted

Figure 5. Menu of the three applications (JUL top left, GTOS top right and INT bottom).

In terms of structure and presentation, there are shared views and elements among the

different applications, making it easier to use the tool across various systems for parallel

use and comparison of appointment volumes and services.

The applications share the following three views (Figure 5). The categories view

is helpful for terminal operator analysis because it allows comparison of the system’s

primary indicators across all categories. The time view supports the terminal operator

in spotting patterns and foreseeing potential issues by making it simpler to comprehend

how indications change over time. The summary view (available only in JUL and GTOS

analytics) provides a high-level overview that enables the port authority to efﬁciently assess

the terminal’s performance by summarizing the KPIs of the system.

Additionally, there are speciﬁc dashboards in each system, adapted to their partic-

ularities: in JUL, there is a dashboard that covers only cancellations; in GTOS, there is a

dashboard that presents the availability of time windows; and in INT, a dashboard that

assesses compliance with the time window.

Several dashboards were developed, each prominently displaying the identiﬁcation

of the source application and the speciﬁc object being analyzed, providing a clear and

organized interface for users to make comparisons and evaluations. The ﬁve most relevant

dashboards, out of a total of ten developed, will be presented next.

The “Categories View” dashboard aims to provide a detailed and versatile analysis

of appointment data across different categories. This dashboard is illustrated when used

in JUL analytics (Figure 6), and displays two indicators based on the selections made in

the left menus. The top graph shows the “Average Appointment Advance”, based on the

category chosen in the top left selection menu (this top graph also provides a color gradient

representation of the Number of Appointments). The bottom graph shows the “Number

of Appointments”, again based on the category chosen in the top left selection menu and

on the selections made in the other two menus. The bottom graph aims to provide a

comprehensive view of the distribution of the number of appointments across different

values of the chosen category. These data can be further analyzed daily, as a total, or as a

percentage of the total, giving users versatile options for assessing and understanding the

appointment data.

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Sustainability 2025, 17, x FOR PEER REVIEW 18 of 28

decision-making processes, enabling users to make data-driven choices tailored to the

speciﬁc trends and characteristics identiﬁed within each category.

In the example of Figure 6, the analysis category is the year (marked in orange). The

upper graph shows that the time in advance of appointments has increased over the years,

but 31.9 min is still a low value, leaving limited ﬂexibility for the terminal operator. The

year with the highest number of appointments is 2022, indicated by the darkest bar in the

upper graph and shown in the lower graph. In the caption of the lower graph, the category

is the type of service (marked in green), and the indicator is on a total basis (marked in

blue). Delivery is the type of service with the highest number of appointments, although

it dropped signiﬁcantly from 2022 to 2023.

The only diﬀerences between this type of dashboard and the other applications are

the indicators analyzed. In the GTOS application, there are the “Average Duration of

Service”, “% Entry and Exit Same Time Window”, and “No. Services”. In the INT

application, there are the “% Scheduled Services”, “No. Appointments”, and “No.

Services”.

Figure 6. Categories view dashboard—JUL analytics.

The “Summary View” dashboard aims to oﬀer a comprehensive overview of the

main performance indicators for the system being analyzed. This dashboard, when

viewed in GTOS analytics (Figure 7), provides information on the daily number of

services (outlined in blue). These services are categorized by type of service and grouped

into containers delivered and picked up per day, allowing for a deep understanding of

the various service activities. This level of detail is crucial for stakeholders, especially the

port authority proﬁle, as it enables them to closely monitor the system’s performance and

make well-informed decisions based on the most accurate and up-to-date data available.

Based on Figure 7’s example, there were, on average, 204 services provided each day,

with delivery and pick-up services accounting for the majority (109), delivery-only

services coming in second with 51 services per day, and pick-up-only services coming in

last with 44 services. This ﬁgure shows that 164 containers were delivered and 174 were

collected daily on average. Furthermore, just 31% of the trucks departed within the same

time window that they arrived, and each service took an average of 58 min.

In the JUL application, this dashboard shows the “No. Appointments”, “ Containers

per Appointment”, “% Cancellation”, and “Average Appointment Advance”.